论文标题
利用与内存网络的蛋白质 - 蛋白质相互作用提取的先验知识
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
论文作者
论文摘要
从生物医学文献中自动提取蛋白质 - 蛋白质相互作用（PPI）为精确医学工作提供了更多支持。本文提出了一种基于内存网络的新型模型（MNM），用于PPI提取，该模型利用了与内存网络有关蛋白质 - 蛋白质对的先验知识。拟议的MNM捕获了与从知识库中学到的知识表示相关的重要上下文线索。先验知识的实体嵌入和关系嵌入均有效地改善PPI提取模型，从而在生物依据的VI PPI数据集上产生了新的最先进的性能。本文还表明，外部内存上的多个计算层优于局部记忆的长期短期内存网络。
Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein-protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.
